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GenAI · LLM Agents · RAG · Evaluation

LLM agents you can put in production

Agents that plan, call tools, and iterate, built on Claude, GPT-4o, Gemini, and open-weight models. Multi-agent orchestration with LangGraph, AutoGen, or CrewAI when the problem is team-shaped. Every project ships with a golden-dataset evaluation harness from week one because agents that cannot be measured cannot be trusted.

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RG INSYS LLP builds GenAI agents and LLM applications that survive contact with production traffic. Single-agent assistants and multi-agent systems with explicit graph control, tool use, structured outputs, evaluation harnesses, guardrails, and observability. Models: Anthropic Claude, OpenAI GPT-4o and o-series, Google Gemini, Mistral Large, self-hosted Llama 3 and Qwen. Frameworks: LangGraph, AutoGen, CrewAI, Anthropic Agent SDK, OpenAI Assistants. UK, US, UAE, and Indian clients in healthcare, insurance, recruitment, fintech, and SaaS rely on us when their PoC needs to graduate to production.

What we deliver
Tool-using agents, multi-agent orchestration, RAG pipelines, structured data extraction, agent evaluation harnesses, guardrails, observability and tracing, prompt and dataset versioning, cost controls per tenant.
Typical timeline
4 weeks for a PoC. 10 to 16 weeks for a production agent integrated with your APIs and approval flows. Ongoing iteration on a monthly retainer.
Pricing from
$12,000 fixed-price PoC. $5,500/month dedicated GenAI engineer with LLM API costs passed through transparently.
Stack
Claude, GPT-4o, Gemini, Mistral, Llama 3, LangGraph, AutoGen, CrewAI, LlamaIndex, pgvector, Pinecone, Qdrant, Langfuse, LangSmith, Anthropic Agent SDK, OpenAI Assistants API.
Compliance-ready for
HIPAA (private model hosting), GDPR (EU residency), SOC 2 patterns. PII redaction, prompt and tool logging policies, on-premise model options when data must not leave your network.
What's included

Agents, evaluated and observable

🧭

Agent design & orchestration

Explicit agent graphs with LangGraph, multi-agent orchestration with AutoGen or CrewAI, or single-vendor stacks (Anthropic Agent SDK, OpenAI Assistants). We pick the lightest framework that fits the problem.

🔧

Tool integrations

Custom tool wrappers for your APIs, your data warehouse (text-to-SQL), your CRM, your knowledge base. MCP-compatible tools where it pays off. Human-approval gates for irreversible actions.

📚

RAG pipelines for agents

Document ingestion, chunking, embeddings, hybrid retrieval (BM25 + vector), reranking, and citations. Agents that can look things up before answering. pgvector / Pinecone / Qdrant by default.

🧪

Evaluation harness

Golden dataset of 30+ representative inputs with acceptance criteria. Runs on every change. Tracks accuracy, completion, tool-call correctness, latency, and cost per task. Regression alerts in CI.

🛡️

Guardrails & safety

Output schema validation, allowed-action lists, PII redaction, plan-and-confirm gates on dangerous tools, prompt-injection mitigation, Llama Guard or custom output classifiers.

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Observability & cost

Every prompt, tool call, and response logged with Langfuse or LangSmith. Per-tenant cost dashboards. Token budget caps. Drift detection on accuracy and cost over time.

Our method

How an agent project actually unfolds

01
Problem framing, week 1

Workshop on the user problem and the path. Decide: workflow, single agent, or multi-agent. Identify tools, data, and approval gates. Output: design spec with explicit failure modes.

02
Golden dataset & harness, week 2

Build a 30+ case evaluation harness with measurable acceptance criteria. Run baseline against 2 to 3 candidate models and frameworks. Pick the winning stack with data, not preference.

03
Working prototype, weeks 2–4

Build the agent with tools, RAG, guardrails, and observability. Integrate against a sandbox of your real system. Run the harness on every change. Deliver a written assessment.

04
Production rollout, weeks 5+

Canary release behind a feature flag, then ramp. Cost caps, per-tenant rate limits, escalation paths. Quarterly model reviews as new versions ship.

Our tech stack for GenAI agents

We deliberately stay portable. Application code goes through thin model abstractions so the underlying provider can be swapped. Evaluation harnesses are framework-agnostic so a future you can move off LangGraph or LangSmith without losing the regression suite. Self-host only when data residency, cost, or quality genuinely demands it.

Anthropic Claude (Sonnet / Opus) OpenAI GPT-4o / o-series Google Gemini Mistral Large Llama 3 (self-hosted) Qwen 2.5 (self-hosted) LangGraph AutoGen CrewAI Anthropic Agent SDK OpenAI Assistants API LlamaIndex pgvector Pinecone Qdrant Langfuse LangSmith MCP tools
Use cases that work

What we actually put in production

📨

Support triage & drafting

Inbound ticket classification, routing, and first-draft replies with cited knowledge-base sources. Human reviews and sends. Cuts response time without removing the human.

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Document workflows

Intake → extract → classify → route → file. Invoices, contracts, claims, KYC packs. Confidence scoring with low-confidence items going to human review queue.

💼

Sales research & outreach

Account research agents that pull from CRM, web, and your knowledge base. Draft personalised outreach. Sales rep reviews and sends. Pipeline activity up, copy quality up.

📊

Text-to-SQL over your data

Internal data-question agents that translate natural language into validated SQL over your warehouse. Read-only by default. Saves analyst time on routine reporting questions.

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Code review & PR drafting

Repo-aware agents that review PRs against your style guide, suggest fixes, and draft small PRs themselves. Humans merge. Pairs well with our own AI-native delivery.

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RFP & contract analysis

Compare an incoming RFP or contract against your standard terms. Flag deviations, propose redlines, summarise commercial risk. Output: a structured review your legal team trusts.

Pricing

Transparent pricing for GenAI & agent work

From $12,000

Fixed-price 4-week PoC including evaluation harness. Or $5,500/month dedicated GenAI engineer with full LLM API costs passed through transparently.

  • 30+ case golden dataset and evaluation harness
  • Honest model and framework comparison with measured numbers
  • Working prototype integrated against a sandbox of your real system
  • Written production rollout plan with cost projections
Full pricing & engagement models →

All pricing transparent. No hidden fees. Free 48-hour written estimate.

FAQ

Common questions about LLM agent work

An LLM application is a single-shot prompt-response system. An LLM agent can plan, use tools, iterate, and route between sub-agents. Agents are useful when the path is not knowable upfront. They cost more per task and are harder to evaluate, so we use them only when a simpler workflow will not do.
LangGraph for explicit graph control, AutoGen or CrewAI for multi-agent orchestration, or the Anthropic Agent SDK / OpenAI Assistants API when one vendor's tool reliability pays off. We avoid lock-in by keeping prompts, tools, and evaluation harnesses portable.
Every agent project ships with an evaluation harness from week one. Golden dataset of inputs and acceptance criteria, run on every change, tracking accuracy, completion, tool-call correctness, and cost per task. Without evaluation, agent projects fail silently in production.
Yes. We deploy with open-weight models (Llama 3, Mistral, Qwen) on your AWS/Azure/GCP account when data must not leave your network. Trade-off: slightly lower top-end reasoning quality, but full data control. We benchmark both options on your real data before recommending.
A 4-week fixed-price engagement: agent design spec, working prototype with tools and orchestration, 30+ case golden dataset, measured accuracy and cost per task, deployment plan with infra projections, and a written assessment of where the agent will struggle in production.
Three layers. Tool design (dangerous actions go through human-approval gates, not free-form text), output validation (structured schemas, allowed-action lists, PII redaction), and observability (every action logged with prompt and tools called, so a human can audit). High-stakes flows add a plan-and-confirm step before execution.
Support triage, sales research and outreach drafting, RFP/contract analysis, code-review assistants, internal data-question answering (text-to-SQL), document workflows, and developer copilots. Often fails: tasks needing perfect accuracy, high-stakes autonomous decisions without escalation.
You do, from day one. All prompts, evaluation datasets, tool wrappers, agent graphs, and infra live in your repository and your cloud account. API keys and model-provider relationships are in your name. We document the system so a competent ML engineer on your side can take it over with two weeks of handover.
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